Complexity is a choice. SASE migrations shouldn’t take years.

For years, the cybersecurity industry has accepted a grim reality: migrating to a zero trust architecture is a marathon of misery. CIOs have been conditioned to expect multi-year deployment timelines, characterized by turning screws, manual configurations, and the relentless care and feeding of legacy SASE vendors.

But at Cloudflare, we believe that kind of complexity is a choice, not a requirement. Today, we are highlighting how our partners are proving that what used to take years now takes weeks. By leveraging Cloudflare One, our agile SASE platform, partners like TachTech and Adapture are showing that the path to safe AI and Zero Trust adoption is faster, more seamless, and more programmable than ever before.

The traditional migration path for legacy SASE products—specifically the deployment of Secure Web Gateway (SWG) and Zero Trust Network Access (ZTNA)—often stretches to 18 months for large organizations. For a CIO, that represents a year and a half of technical debt and persistent security gaps.

By contrast, partners like TachTech and Adapture are proving that this marathon of misery is not a technical necessity. By using a unified connectivity cloud, they have compressed these timelines from 18 months Continue reading

Modernizing with agile SASE: a Cloudflare One blog takeover

Return to office has stalled for many, and the “new normal” for what the corporate network means is constantly changing.  In 2026, your office may be a coffee shop, your workforce includes autonomous AI agents, and your perimeter is wherever the Internet reaches. This shift has forced a fundamental change in how we think about security, moving us toward a critical new architecture: agile SASE.

For too long, organizations have struggled under a 'fragmentation penalty,' juggling a patchwork of legacy hardware and Virtual Private Network (VPN) concentrators. These tools don't just require massive upfront investment; they create a mountain of technical debt — the cumulative cost of maintaining thousands of conflicting firewall rules, manual patches, and aging hardware that can’t support AI-scale traffic.

First-generation SASE providers promised a cure, but often just moved the mess to the cloud. By treating every data center as an isolated island, they’ve replaced hardware silos with operational silos. The result isn't a lack of visibility, but a lack of actionability: plenty of data, but no single way to enforce a consistent policy across a borderless enterprise.

Our customers have told us they need  an agile and composable platform. This week, we are announcing Continue reading

Beyond the ban: A better way to secure generative AI applications

The revolution is already inside your organization, and it's happening at the speed of a keystroke. Every day, employees turn to generative artificial intelligence (GenAI) for help with everything from drafting emails to debugging code. And while using GenAI boosts productivity—a win for the organization—this also creates a significant data security risk: employees may potentially share sensitive information with a third party.

Regardless of this risk, the data is clear: employees already treat these AI tools like a trusted colleague. In fact, one study found that nearly half of all employees surveyed admitted to entering confidential company information into publicly available GenAI tools. Unfortunately, the risk for human error doesn’t stop there. Earlier this year, a new feature in a leading LLM meant to make conversations shareable had a serious unintended consequence: it led to thousands of private chats — including work-related ones — being indexed by Google and other search engines. In both cases, neither example was done with malice. Instead, they were miscalculations on how these tools would be used, and it certainly did not help that organizations did not have the right tools to protect their data. 

While the instinct for many may be to deploy Continue reading

Improving Data Loss Prevention accuracy with AI-powered context analysis

We are excited to announce our latest innovation to Cloudflare’s Data Loss Prevention (DLP) solution: a self-improving AI-powered algorithm that adapts to your organization’s unique traffic patterns to reduce false positives. 

Many customers are plagued by the shapeshifting task of identifying and protecting their sensitive data as it moves within and even outside of their organization. Detecting this data through deterministic means, such as regular expressions, often fails because they cannot identify details that are categorized as personally identifiable information (PII) nor intellectual property (IP). This can generate a high rate of false positives, which contributes to noisy alerts that subsequently may lead to review fatigue. Even more critically, this less than ideal experience can turn users away from relying on our DLP product and result in a reduction in their overall security posture. 

Built into Cloudflare’s DLP Engine, AI enables us to intelligently assess the contents of a document or HTTP request in parallel with a customer’s historical reports to determine context similarity and draw conclusions on data sensitivity with increased accuracy.

In this blog post, we’ll explore DLP AI Context Analysis, its implementation using Workers AI and Vectorize, and future improvements we’re developing.